https://github.com/tuantdang/v3d-slam
공식 코드V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting
Papers with Code 아카이브
Simultaneous Localization and Mapping
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 0
- 보존된 평가 기록
- 0
Simultaneous localization and mapping (SLAM) is the task of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
<span style="color:grey; opacity: 0.6">( Image credit: ORB-SLAM2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tumftm/flexcloud
공식 코드FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
https://github.com/uestc-nnlab/lgu-slam
공식 코드https://github.com/umich-bipedlab/optimal_shape_generation
공식 코드https://github.com/umich-bipedlab/optimal_shape_global_pose_estimation
공식 코드https://github.com/urbste/MultiCol-SLAM
공식 코드MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
https://github.com/url-kaist/Struct-MDC
공식 코드https://github.com/vis4rob-lab/hyperion
공식 코드Hyperion - A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM
https://github.com/vision4robotics/KAOT-tracker
공식 코드https://github.com/wang-chen/airloop
공식 코드https://github.com/wh200720041/SRLCD
공식 코드Online Visual Place Recognition via Saliency Re-identification
https://github.com/wh200720041/iscloam
공식 코드Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
https://github.com/wu-cvgl/mba-slam
공식 코드MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation
https://github.com/xiaohao-xu/slam-under-perturbation
공식 코드Customizable Perturbation Synthesis for Robust SLAM Benchmarking
https://github.com/yan99033/CNN-SVO
공식 코드CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction
https://github.com/yanmin-wu/EAO-SLAM
공식 코드EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association
https://github.com/youmi-zym/go-slam
공식 코드GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
https://github.com/yuhaozhang7/NGD-SLAM
공식 코드https://github.com/yungecui/bow3d
공식 코드BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM
https://github.com/yutongwangbit/voom
공식 코드VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks
https://github.com/zdzhaoyong/GSLAM
공식 코드https://github.com/zhangganlin/gloire-slam
공식 코드GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
https://github.com/zhengxi-git/phase-slam
공식 코드https://github.com/zhicongsun/d4dgs-slam
공식 코드Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM
https://github.com/zju3dv/DeFlowSLAM
공식 코드D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance
https://gitee.com/whitbyli/mono-orbslam2
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
https://github.com/Akilax0/Pseudo-RGB-D-for-Self-Improving-Monocular-SLAM-and-Depth-Prediction
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
https://github.com/AndrewGavril/Kimera-VIO-RaspberryPi
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/CanCanZeng/LearnVIORB
https://github.com/EbadSyed/spadRGBD
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/EmergentSystemLabStudent/SpCoSLAM
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
https://github.com/EmergentSystemLabStudent/SpCoSLAM_Lets
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
https://github.com/FreeformRobotics/semantic-histogram-based-global-localization
https://github.com/HKUST-Aerial-Robotics/ESVO
https://github.com/HyHuang1995/gmmloc
GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models
https://github.com/JiawangBian/SC-SfMLearner-Release
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.